Prevention of RhD Alloimmunization: A Comparison of Four National Guidelines
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compare national guidelines on the prevention of RhD alloimmunization. STUDY DESIGN: We performed a review of four national guidelines on prevention of alloimmunization from the American Congress of Obstetricians and Gynecologists, Royal College of Obstetricians and Gynaecologists, Society of Obstetricians and Gynaecologists of Canada, and The Royal Australian and New Zealand College of Obstetricians and Gynaecologists. We compared the indications/contraindications, timing, dosing, formulation and route of anti-D immune globulin, and management of unique circumstances. The references were compared with regard to the number of randomized control trials, Cochrane Reviews, and systematic reviews/meta-analyses cited. RESULTS: Variation exists in recommendations on the timing and need for consent prior to routine antenatal anti-D immune globulin administration, prophylaxis for unique circumstances (e.g., threatened abortion < 12 weeks, complete molar pregnancy), and the use of cell-free fetal DNA testing for fetal RhD genotype. CONCLUSION: These variations in recommendations reflect the heterogeneity of the literature on the prevention of alloimmunization and highlight the need for synthesis of evidence to create an international guideline on prevention of alloimmunization. This may improve safety, quality, optimize outcomes, and stimulate future trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".